Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Safe Element Screening for Submodular Function Minimization
Authors: Weizhong Zhang, Bin Hong, Lin Ma, Wei Liu, Tong Zhang
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results on both synthetic and real datasets demonstrate the significant speedups gained by our approach. |
| Researcher Affiliation | Collaboration | 1Tencent AI Lab 2State Key Lab of CAD&CG, Zhejiang University. |
| Pseudocode | Yes | Algorithm 2 Inactive and Active Element Screening |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We perform experiments on a synthetic dataset named two-moons... We use five images (included in the supplemental material) in (Rother et al., 2004) to evaluate IAES. |
| Dataset Splits | No | The paper describes the datasets used but does not specify any training, validation, or test splits. |
| Hardware Specification | Yes | We write the code in Matlab and perform all the computations on a single core of Intel(R) Core(TM) i7-5930K 3.50GHz, 32GB MEM. |
| Software Dependencies | No | The paper mentions "We write the code in Matlab" but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | We set the accuracy tolerance ϵ to be 10 6. ... In our experiment, we set ρ = 0.5 and it achieves a good performance. |